PAOLO DELL'OLMO

For the Students of Data Driven Decision Making (DDDM) and Laboratory of Data Driven Decision Making (DDDMLAB) a.a. 2025-26 I semester.
Data Driven Decision Making: first lesson 22 September 2025
Laboratory of Data Driven Decision Making: first lesson 23 September 2025

Timetable Data Driven Decision Making:
Monday 2:00 pm - 4:00 pm Edificio: CU002 Aula V
Thursday 2:00 pm - 4:00 pm Edificio: CU002 Aula V

Timetable  Laboratory of Data Driven Decision Making:
Tuesday ore12:00 am - 2:00 pm Edificio: CU002 Aula XIII 
 
Your physical presence during the lessons is strongly recommended, especially for the Laboratory of DDDM.
 
The teaching method adopted foresees explanations by the teacher with references to teaching material (slides and books) and markedly interactive moments in the classroom and in the laboratory with reference to the adoption of the course methods for real problems.
 
Suggested readings:
 
Denis Bouyssou and Philippe Vinke, Binary Relations and Preference Modeling
 
Simon French Decision Theory: a Introduction to the Mathematics of Rationality
Chapters 3 (paragraph 3.7 excluded), Chapters 4 (paragraph 4.5 excluded)
 
C.H. Antunes, M.J. Alves, J. Climacao
Multiobjective Linear and Integer Programming
Chapters 1, 2, 3, and the paragraphs 4.1, 4.2, 6.1, 6.2 A.
 
Ishizaka, P. Nemery
Multi-Criteria Decision Analysis
Chapters 1, 2, 4, 6, 7, 9
D. Bertsimas et al. The Analytics Edge Chapters 10, 12, 13, 14
 
The exam consists in a written text with some exercises and questions (open and with multiple choices). 
 
For the DDDM LAB we will work togher in the class using different software tools. The Lab will be concluded with an original project (small groups are allowed). To get the (3CFU) a final practical test has to be passed succefully.
 
Office hours (for students): Thesday at 3 pm, room 42, fourth floor, Dipartimento di Scienze Statistiche, please email me in advance.
 
Course Contents:

1. Introduction to Data Driven Decision Making
2. Introduction to Part I
3. Data, Objectives, Measurements and Decisions: The Balanced Scorecard
4. Choice Problems, Decision Maker Preferences and Ordinal Value Functions
5. Properties of Preference Relations 
6. Weak Orders and Ordinal Value Functions 
7. Semiorders and Interval Orders – Combinatorial and Analytic Representations
8. Multi Attribute Preferences   
9. Analytic Hierarchy Process - AHP 
10. ELECTRE Method 
11. UTA+ Method
12. Promethee Method 
13. Introduction to Part II 
14. Mathematical Programming for Decision Making
15. Goal Programming 
16. Integer and 0-1 Programming in Decision Making
17. Multiobjective Optimization
18. Exploring Objective Space: Scalarizing Techniques 
19. Stem Method 
20. Zionts-Wallenius Method
21. Computational Complexity in Decision Making
22. Introduction to Part III
23. Collective Decision Making and Collective Intelligence
24. Metric Approach to Collective Decision Making
25. Fraud Detection and Relational Data Analysis 
26. Auctions and Google Ads 
27. Analytics for Kidney Allocation 
28. Recommendation Systems 

Per gli studenti di Networks Analytics a.a. 2025-26 II semestre
 
Inizio lezioni:  
Termine lezioni:  
 
Orario Lezioni:
Lunedi ore 14:00-16:00 Edificio: CU007 Aula 13
(Palazzina Tuminelli)
Giovedi ore 15:00-19:00 Edificio: RM025 Aula informatica 15
 
Si raccomanda fortemente la frequenza in presenza, in particolare per le attività in aula informatica.
 
Bibliografia:
 

Cambridge University Press
 
David Easley,Jon Kleinberg
Networks, Crowds, and Markets: Reasoning about a Highly Connected World
Cambridge University Press
 
Eric D. Kolaczyk
Statistical Analysis of Network Data
Methods and Models, Springer
 
 
Contenuti e loro sequenza temporale:
 
1 Introduzione 
2 Teoria dei Grafi (1) 
3 Teoria dei Grafi (2) 
4 Algoritmi di Visita di Grafi 
5 Alberi ricoprenti 
6 Cammini minimi 
7 Random Networks  
8 Random Networks e Gephi 
9 Random Networks (2) 
10 Small World and the Watts Strogatz Model 
11 Esercitazione Analisi Reti 
12 Scale Free Networks (1) 
13 Scale Free Networks (2) e Progetti 
14 Lab Network Analysis - Practice 
15 Scale Free Networks Generation e Barabasi Albert Model 
16 Models of Preferential Attachment 
17 Centrality Measures
18 Introduzione a NetworkX 
19 Grafi Triangolati 
20 Esercitazione NetworkX 
21 Communities (1) 
22 Communities (2) - Maximal Cliques 
23 Network Clustering 
24 Degree Correlation (1) 
25 Degree Correlation (2) 
26 Network Robustness (1) 
27 Network Robustness (2) 
28 Spreading Phenomena (2) 
29 Spreading Phenomena (3)  
30 Network Robustness 
31 Cascading Behaviour in Networks 
32 Cascading Behaviour in Networks 2 
 
Il metodo didattico adottato prevede spiegazioni da parte del docente con riferimenti a materiale didattico (slides e libri) e dei momenti marcatamene interattivi in aula e in laboratorio con riferimento all'utilizzo delle metodiche del corso per problemi reali.
 
L'esame consiste in un test scritto con esercizi, domande a riposta aperta e domande chiuse e la realizzazione di un progetto. Il progetto permette un miglioramento del voto del test scritto fino a 2 punti.
 
Ricevimento studenti: Martedi alle 15, stanza 42, quarto piano, Dipartimento di Scienze Statistiche, preferibilemente inviare una mail preventiva.